Reihaneh Rabbany

hussein.abdallah@mail.concordia.ca

Biography

Reihaneh Rabbany is an assistant professor at the School of Computer Science, McGill University, and a core academic member of Mila – Quebec Artificial Intelligence Institute. She is also a Canada CIFAR AI Chair and on the faculty of McGill’s Centre for the Study of Democratic Citizenship.

Before joining McGill, Rabbany was a postdoctoral fellow at the School of Computer Science, Carnegie Mellon University. She completed her PhD in the Department of Computing Science at the University of Alberta.

Rabbany heads McGill’s Complex Data Lab, where she conducts research at the intersection of network science, data mining and machine learning, with a focus on analyzing real-world interconnected data and social good applications.

Current Students

Hussein Abdallah

Collaborating researcher - Concordia University

Aurélien Bück-Kaeffer

Master's Research - McGill University

Jacob Chmura

Master's Research - McGill University

Principal supervisor :

PhD - McGill University

Co-supervisor :

Adriana Romero Soriano

Collaborating Alumni - McGill University

Co-supervisor :

Research Intern - McGill University

Master's Research - McGill University

Victor Livernoche

PhD - McGill University

Sitao Luan

Postdoctorate - McGill University

Principal supervisor :

Guillaume Rabusseau

Shahrad Mohammadzadeh

Master's Research - McGill University

Co-supervisor :

Collaborating researcher - McGill University

Master's Research - McGill University

Website

Soroush Omranpour

Collaborating Alumni - McGill University

Co-supervisor :

Guillaume Rabusseau

kellin.pelrine@mila.quebec

PhD - McGill University

Website

Farimah Poursafaei

Collaborating Alumni - McGill University

Collaborating researcher

Collaborating researcher - McGill University University

Sneheel Sarangi

Collaborating researcher - McGill University

sneheelsarangi@gmail.com

Research Intern - McGill University

Vidya Sujaya

Master's Research - McGill University

Camille Thibault

Master's Research - Université de Montréal

Principal supervisor :

jacob.mila.handle@tianshome.com

Collaborating researcher - McGill University

Collaborating researcher - Université de Montréal

Principal supervisor :

PhD - McGill University

Jie Zan

Research Intern - McGill University

jie.zan@mail.mcgill.ca

Sveta Zhuk

Master's Research - Université de Montréal

Principal supervisor :

Flight-SEIR: Incorporating Flight Data to Improve Epidemiological Modelling and Disease Outbreak Prevention

Blog Posts

August 3, 2021

Shenyang Huang

Reihaneh Rabbany

Read the article

Publications

UTG: Towards a Unified View of Snapshot and Event Based Models for Temporal Graphs

Emanuele Rossi

Many real world graphs are inherently dynamic, constantly evolving with node and edge additions. These graphs can be represented by temporal… (see more) graphs, either through a stream of edge events or a sequence of graph snapshots. Until now, the development of machine learning methods for both types has occurred largely in isolation, resulting in limited experimental comparison and theoretical crosspollination between the two. In this paper, we introduce Unified Temporal Graph (UTG), a framework that unifies snapshot-based and event-based machine learning models under a single umbrella, enabling models developed for one representation to be applied effectively to datasets of the other. We also propose a novel UTG training procedure to boost the performance of snapshot-based models in the streaming setting. We comprehensively evaluate both snapshot and event-based models across both types of temporal graphs on the temporal link prediction task. Our main findings are threefold: first, when combined with UTG training, snapshot-based models can perform competitively with event-based models such as TGN and GraphMixer even on event datasets. Second, snapshot-based models are at least an order of magnitude faster than most event-based models during inference. Third, while event-based methods such as NAT and DyGFormer outperforms snapshot-based methods on both types of temporal graphs, this is because they leverage joint neighborhood structural features thus emphasizing the potential to incorporate these features into snapshotbased models as well. These findings highlight the importance of comparing model architectures independent of the data format and suggest the potential of combining the efficiency of snapshot-based models with the performance of event-based models in the future.

2024-07-17

ArXiv (preprint)

Web Retrieval Agents for Evidence-Based Misinformation Detection

Jacob-Junqi Tian

Hao Yu

Yury Orlovskiy

Tyler Vergho

Mauricio Rivera

Mayank Goel

2024-07-10

colmweb.org/COLM/2024/Conference (accepted)

openreview.net

Regional and Temporal Patterns of Partisan Polarization during the COVID-19 Pandemic in the United States and Canada

Anne Imouza

Maximilian Puelma Touzel

C'ecile Amadoro

Gabrielle Desrosiers-Brisebois

Sacha Lévy

Public health measures were among the most polarizing topics debated online during the COVID-19 pandemic. Much of the discussion surrounded … (see more)specific events, such as when and which particular interventions came into practise. In this work, we develop and apply an approach to measure subnational and event-driven variation of partisan polarization and explore how these dynamics varied both across and within countries. We apply our measure to a dataset of over 50 million tweets posted during late 2020, a salient period of polarizing discourse in the early phase of the pandemic. In particular, we examine regional variations in both the United States and Canada, focusing on three specific health interventions: lockdowns, masks, and vaccines. We find that more politically conservative regions had higher levels of partisan polarization in both countries, especially in the US where a strong negative correlation exists between regional vaccination rates and degree of polarization in vaccine related discussions. We then analyze the timing, context, and profile of spikes in polarization, linking them to specific events discussed on social media across different regions in both countries. These typically last only a few days in duration, suggesting that online discussions reflect and could even drive changes in public opinion, which in the context of pandemic response impacts public health outcomes across different regions and over time.

2024-07-03

ArXiv (preprint)

Regional and Temporal Patterns of Partisan Polarization during the COVID-19 Pandemic in the United States and Canada

Anne Imouza

Maximilian Puelma Touzel

C'ecile Amadoro

Gabrielle Desrosiers-Brisebois

Sacha Lévy

2024-07-03

ArXiv (preprint)

Game On, Hate Off: A Study of Toxicity in Online Multiplayer Environments

Nicolas Grenon-Godbout

2024-06-28

Games Res. Pract. (published)

MiNT: Multi-Network Training for Transfer Learning on Temporal Graphs

Kiarash Shamsi

Tran Gia Bao Ngo

Razieh Shirzadkhani

Poupak Azad

Baris Coskunuzer

Cuneyt Gurcan Akcora

2024-06-14

ArXiv (preprint)

MiNT: Multi-Network Training for Transfer Learning on Temporal Graphs

Kiarash Shamsi

Tran Gia Bao Ngo

Razieh Shirzadkhani

Poupak Azad

Baris Coskunuzer

Cuneyt Gurcan Akcora

2024-06-14

ArXiv (preprint)

TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs

Julia Gastinger

Shenyang Huang

Mikhail Galkin

Erfan Loghmani

Ali Parviz

Farimah Poursafaei

Jacob Danovitch

Emanuele Rossi

Ioannis Koutis

Heiner Stuckenschmidt

Guillaume Rabusseau

Multi-relational temporal graphs are powerful tools for modeling real-world data, capturing the evolving and interconnected nature of entiti… (see more)es over time. Recently, many novel models are proposed for ML on such graphs intensifying the need for robust evaluation and standardized benchmark datasets. However, the availability of such resources remains scarce and evaluation faces added complexity due to reproducibility issues in experimental protocols. To address these challenges, we introduce Temporal Graph Benchmark 2.0 (TGB 2.0), a novel benchmarking framework tailored for evaluating methods for predicting future links on Temporal Knowledge Graphs and Temporal Heterogeneous Graphs with a focus on large-scale datasets, extending the Temporal Graph Benchmark. TGB 2.0 facilitates comprehensive evaluations by presenting eight novel datasets spanning five domains with up to 53 million edges. TGB 2.0 datasets are significantly larger than existing datasets in terms of number of nodes, edges, or timestamps. In addition, TGB 2.0 provides a reproducible and realistic evaluation pipeline for multi-relational temporal graphs. Through extensive experimentation, we observe that 1) leveraging edge-type information is crucial to obtain high performance, 2) simple heuristic baselines are often competitive with more complex methods, 3) most methods fail to run on our largest datasets, highlighting the need for research on more scalable methods.

2024-06-14

ArXiv (preprint)

Towards Neural Scaling Laws for Foundation Models on Temporal Graphs

Razieh Shirzadkhani

Tran Gia Bao Ngo

Kiarash Shamsi

Poupak Azad

Baris Coskunuzer

Cuneyt Gurcan Akcora

The field of temporal graph learning aims to learn from evolving network data to forecast future interactions. Given a collection of observe… (see more)d temporal graphs, is it possible to predict the evolution of an unseen network from the same domain? To answer this question, we first present the Temporal Graph Scaling (TGS) dataset, a large collection of temporal graphs consisting of eighty-four ERC20 token transaction networks collected from 2017 to 2023. Next, we evaluate the transferability of Temporal Graph Neural Networks (TGNNs) for the temporal graph property prediction task by pre-training on a collection of up to sixty-four token transaction networks and then evaluating the downstream performance on twenty unseen token networks. We find that the neural scaling law observed in NLP and Computer Vision also applies in temporal graph learning, where pre-training on greater number of networks leads to improved downstream performance. To the best of our knowledge, this is the first empirical demonstration of the transferability of temporal graphs learning. On downstream token networks, the largest pre-trained model outperforms single model TGNNs on thirteen unseen test networks. Therefore, we believe that this is a promising first step towards building foundation models for temporal graphs.

2024-06-14

ArXiv (preprint)

Static graph approximations of dynamic contact networks for epidemic forecasting

Razieh Shirzadkhani

Shenyang Huang

Abby Leung

2024-05-22

Scientific Reports (published)

T-NET: Weakly Supervised Graph Learning for Combatting Human Trafficking

Pratheeksha Nair

Javin Liu

Catalina Vajiac

Andreas Olligschlaeger

Duen Horng Chau

Mirela T. Cazzolato

Cara Jones

Christos Faloutsos

Human trafficking (HT) for forced sexual exploitation, often described as modern-day slavery, is a pervasive problem that affects millions o… (see more)f people worldwide. Perpetrators of this crime post advertisements (ads) on behalf of their victims on adult service websites (ASW). These websites typically contain hundreds of thousands of ads including those posted by independent escorts, massage parlor agencies and spammers (fake ads). Detecting suspicious activity in these ads is difficult and developing data-driven methods is challenging due to the hard-to-label, complex and sensitive nature of the data. In this paper, we propose T-Net, which unlike previous solutions, formulates this problem as weakly supervised classification. Since it takes several months to years to investigate a case and obtain a single definitive label, we design domain-specific signals or indicators that provide weak labels. T-Net also looks into connections between ads and models the problem as a graph learning task instead of classifying ads independently. We show that T-Net outperforms all baselines on a real-world dataset of ads by 7% average weighted F1 score. Given that this data contains personally identifiable information, we also present a realistic data generator and provide the first publicly available dataset in this domain which may be leveraged by the wider research community.

2024-03-24

Proceedings of the AAAI Conference on Artificial Intelligence (published)

T-NET: Weakly Supervised Graph Learning for Combatting Human Trafficking

Pratheeksha Nair

Javin Liu

Catalina Vajiac

Andreas Olligschlaeger

Duen Horng Chau

Mirela T. Cazzolato

Cara Jones

Christos Faloutsos

2024-03-24

AAAI Conference on Artificial Intelligence (published)